The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 08, 2026

Filed:

Jan. 19, 2022
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

XI Yun, Redmond, WA (US);

Jiantao Sun, Bellevue, WA (US);

Zheng Chen, Bellevue, WA (US);

Kaushik Chakrabarti, Bellevue, WA (US);

Leon Melvin Romaniuk, Snohomish, WA (US);

Pingjun Hu, Sammamish, WA (US);

Mingyu Wang, Issaquah, WA (US);

Wei Li, Bellevue, WA (US);

Yaxi Li, Bellevue, WA (US);

Abhilash Srivastava, Bellevue, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/2457 (2019.01); G06F 16/31 (2019.01); G06F 16/332 (2019.01); G06F 16/334 (2025.01); G06F 16/901 (2019.01); G06F 16/9535 (2019.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01);
U.S. Cl.
CPC ...
G06F 16/24578 (2019.01); G06F 16/316 (2019.01); G06F 16/3325 (2019.01); G06F 16/334 (2019.01); G06F 16/9024 (2019.01); G06F 16/9535 (2019.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01);
Abstract

Systems are configured for generating and utilizing training data to train learn-to-rank type models in a manner that preserves privacy of client data used for generating the training data. The systems extract features and patterns of the user queries, search results and user interactions with the search results without tracking, storing or transmitting underlying values of the user data to preserve privacy of the user data. Systems are also configured to infer search result quality based on at least the user behavior data, and optionally query intentions, and to generate and label corresponding training data accordingly. This training data is applied to learn-to-rank type models to train the learn-to-rank type model to improve search quality of search results provided by the learn-to-rank type models when new user queries are processed that having features and patterns corresponding to the filtered and labelled training data.


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